Perceptions of the determinants of health across income and urbanicity levels in eight countries.

BACKGROUND: A clear understanding of public perceptions of the social determinants of health remains lacking. This paper aimed to describe the relationship between income and urbanicity levels and public views of the determinants of health in eight middle-and high-income countries that varied across multiple characteristics. METHODS: We conducted a cross-sectional online survey in Brazil, China, Germany, Egypt, India, Indonesia, Nigeria, and the United States. Respondents were asked to select what they considered to be most important for health out of a list of ten determinants. We stratified the results by income and urbanicity levels and tested significance of differences between groups using two-tailed χ2 tests. Multivariable logistic regression models tested associations between demographic factors and the likelihood of respondents selecting the genetics, healthcare, income and wealth, or social support determinants. RESULTS: Here we show 8753 respondents across eight countries. Rankings of determinants are similar across income groups, except for two determinants. Respondents in the highest income group rank genetics in higher proportions (32.4%, 95%CI: 29.0%,35.8%) compared to other income groups. Conversely, those in lowest income group rank social support more frequently (27.9%, 95%CI: 25.3%,30.7%) than other income groups. Those living in urban settings rank healthcare in higher proportions (61.2%, 95%CI: 59.0%,63.4%) compared to non-urban respondents; meanwhile, higher proportions (26.6%, 95%CI: 24.9%,28.3%) of non-urban respondents rank social support as important for health compared to urban respondents. CONCLUSION: Demographic factors play a role in shaping public views of what affects health. Advancing public understanding about determinants of health requires tailoring public health messaging to account for socioeconomic position within a population.

Various factors determine our health, such as our environment, education, and genetics. Here, we explore how people from different income and urban backgrounds view the factors that affect health. We surveyed participants from Brazil, China, Germany, Egypt, India, Indonesia, Nigeria, and the United States. Respondents selected the most important factors for health from a list of ten choices. Overall, people's views on these factors were comparable across income groups, with exceptions. Those with higher incomes tended to emphasize the importance of genetics more, while lower-income individuals valued social support. People in urban areas prioritized healthcare, while those in non-urban areas valued social support. These findings suggest that factors such as income and where people live influence their views about health determinants. These factors should be considered in public health messaging and strategies to promote health amongst different groups.

Determinants of private-sector antibiotic consumption in India: findings from a quasi-experimental fixed-effects regression analysis using cross-sectional time-series data, 2011-2019.

The consumption of antibiotics varies between and within countries. However, our understanding of the key drivers of antibiotic consumption is largely limited to observational studies. Using Indian data that showed substantial differences between states and changes over years, we conducted a quasi-experimental fixed-effects regression study to examine the determinants of private-sector antibiotic consumption. Antibiotic consumption decreased by 10.2 antibiotic doses per 1000 persons per year for every ₹1000 (US$12.9) increase in per-capita gross domestic product. Antibiotic consumption decreased by 46.4 doses per 1000 population per year for every 1% increase in girls' enrollment rate in tertiary education. The biggest determinant of private sector antibiotic use was government spending on health-antibiotic use decreased by 461.4 doses per 1000 population per year for every US$12.9 increase in per-capita government health spending. Economic progress, social progress, and increased public investment in health can reduce private-sector antibiotic use.

Pandemic stressors and mental health indicators in eight countries.

PURPOSE: The Covid-19 pandemic has exacted a significant physical, financial, social, and emotional toll on populations throughout the world. This study aimed to document the association between pandemic stressors and mental health during the pandemic across countries that differ in cultural, geographic, economic, and demographic factors. METHODS: We administered an online survey randomly in Brazil, China, Germany, Egypt, India, Indonesia, Nigeria, and the United States from September 2020 to November 2020. This survey included questions on Covid-19-related stressors as well as the Patient Health Questionnaire-2 and the Primary Care PTSD Checklist to screen for depression and post-traumatic stress disorder (PTSD) symptoms, respectively. We performed bivariable and multivariable regression analyses to assess the prevalence and odds ratios of overall depression symptoms and probable PTSD and in relation to stressors across countries. RESULTS: Among 8754 respondents, 28.9% (95% CI 27.5-30.0%) experienced depression symptoms, and 5.1% (95% CI 4.5-6.0%) experienced probable PTSD. The highest prevalence of depression symptoms was in Egypt (41.3%, 95% CI 37.6-45.0%) and lowest in the United States (24.9%, 95% CI 22.3-27.7%). The highest prevalence of probable PTSD was in Brazil (7.3%, 95% CI 5.6-9.4%) and the lowest in China (1.2%, 95% CI 0.7-2.0%). Overall, experiencing six or more Covid-19-related stressors was associated with both depression symptoms (OR 1.90, 95% CI 1.46-2.48) and probable PTSD (OR 13.8, 95% CI 9.66-19.6). CONCLUSION: The association between pandemic related stressors and the burden of adverse mental health indicators early in the Covid-19 pandemic transcended geographic, economic, cultural, and demographic differences between countries. The short-term and long-term impacts of the pandemic on mental health should be incorporated in efforts to tackle the consequences of Covid-19.

Hypertension control rate in India: systematic review and meta-analysis of population-level non-interventional studies, 2001-2022.

Background: Hypertension is a significant contributor to mortality in India. Achieving better hypertension control rate at the population level is critical in reducing cardiovascular morbidity and mortality. Methods: Hypertension control rate was defined as the proportion of patients with their blood pressure under control (systolic blood pressure <140 mmHg and diastolic blood pressure <90 mmHg). We conducted a systematic review and meta-analysis of community-based, non-interventional studies published after 2001 that reported hypertension control rates. We searched PubMed, Embase, and Web of Science databases, and grey literature, and extracted data using a common framework, and summarized the study characteristics. We conducted random-effects meta-analysis using untransformed hypertension control rates and reported the overall summary estimates and subgroup estimates of control rates as percentages and 95% confidence intervals. We also conducted mixed-effects meta-regression with sex, region, and study period as covariates. The risk of bias was assessed, and level of evidence was summarized using SIGN-50 methodology. The protocol was pre-registered with PROSPERO, CRD42021267973. Findings: The systematic review included 51 studies (n = 338,313 hypertensive patients). 21 studies (41%) reported poorer control rates among males than females, and six studies (12%) reported poorer control rates among rural patients. The pooled hypertension control rate in India during 2001-2020 was 17.5% (95% CI: 14.3%-20.6%)-with significant increase over the years, reaching 22.5% (CI: 16.9-28.0%) in 2016-2020. Sub-group analysis showed significantly better control rates in the South and West regions, and significantly poorer control rates among males. Very few studies reported data on social determinants or lifestyle risk factors. Interpretation: Less than one-fourth of hypertensive patients in India had their blood pressure under control during 2016-2020. Although the control rate has improved compared to previous years, substantial differences exist across regions. Very few studies have examined the lifestyle risk factors and social determinants relevant to hypertension control in India. The country needs to develop and evaluate sustainable, community-based strategies and programs to improve hypertension control rates. Funding: Not applicable.

Prevalence and associated factors of cardiovascular diseases among men and women aged 45 years and above: Analysis of the longitudinal ageing study in India, 2017-2019.

OBJECTIVE: Epidemiological studies on the prevalence and associated factors of cardiovascular diseases (CVDs) representative of all states of India among middle-aged and elderly are not much reported. The present study estimates the prevalence and associated factors of cardiovascular diseases across Indian states among men and women aged ≥45 years. METHODS: We used data from the Longitudinal Ageing Study in India wave 1 (2017-2019), which included a final analytical sample size of 56,935 adults and their spouses aged 45 years and above. We estimated CVDs prevalence for sociodemographic and behavioural variables, and multivariable logistic regression was used to assess the association between behavioural factors and CVDs in both men and women. RESULTS: The prevalence of CVDs was 5.2% among adults ≥45 years (women: 4.6%; men: 5.9%), hypertension was 46.7% (women:48.9%; men:44%). Men and women have a similar prevalence of diabetes (11.9%) and cholesterol (2.3%). Prevalence of physical inactivity was 30.3% (women:27%; men:34.1%). Hypertension (adjusted odds ratio; aOR women:2.60, 95% CI: 2.08-3.25, men:1.88, 95% CI 1.54-2.29), hypercholesterolemia (aOR women:1.70; 95% CI 1.07-2.69, men 3.55; 95% CI 2.66-4.74), diabetes (aOR women:2.53; 95% CI 1.83-3.51, men:1.77 95% CI 1.44-2.17), obesity, physical inactivity, and smoking in men were significantly associated with CVDs. CONCLUSION: The prevalence of CVDs and lifestyle risk factors among middle-aged and elderly poses severe concerns regarding noncommunicable disease (NCD) healthcare services provided in a lower-middle-income country like India. The key to preventing CVDs is controlling hypertension, diabetes, hypercholesterolemia, and increasing physical activity among adults aged ≥45 years.

Clinico-epidemiological profile and outcomes of adults with COVID-19: A hospital-based retrospective study in Kerala, India.

Introduction: The clinical and epidemiological presentations of patients with coronavirus disease 2019 (COVID-19) in India is still not well explored. We studied the epidemiological and clinical profile and outcomes of COVID-19 patients admitted to a tertiary care private hospital in Kerala, India. Methods: In this retrospective study, we analyzed data of 476 adult (≥18 years) COVID-19 patients admitted to a tertiary care hospital in Kerala from September 1, 2020 to March 31, 2021. The patients were categorized into mild, moderate, and severe cases and followed till discharge or death. Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 23.0 with a significance set at P < 0.05. Results: The median age was 57 years (56% men). Mild, moderate, and severe cases accounted for 17%, 65%, and 18%, respectively. Around 75% had at least one comorbidity, and 51% had multiple comorbidities. The most common comorbidities were diabetes (45%), hypertension (44%), dyslipidemia (15%), and cardiac problems (12%). The elevated D-dimer values among patients in different categories were significantly different, with 74% in severe, 46% in moderate, and 19% in mild category patients. Serum ferritin, C-reactive protein, lactic acid dehydrogenase, and neutrophil to lymphocyte ratio values were significantly higher for severely ill patients. Thirty deaths (67% men) occurred during the study period, with a case fatality rate of 6.3%. Mortality mainly happened in the older age group (80%) and those with multimorbidity (90%). Conclusion: Age and multimorbidity are the major contributing factors for death in hospitalized COVID-19 patients. Generalization of the findings necessitates well-designed large-scale studies.

Consumption of systemic antibiotics in India in 2019.

Background: Inappropriate use of antibiotics is a significant driver of antibiotic resistance in India. Largely unrestricted over-the-counter sales of most antibiotics, manufacturing and marketing of many fixed-dose combinations (FDC) and overlap in regulatory powers between national and state-level agencies complicate antibiotics availability, sales, and consumption in the country. Methods: We analyzed cross-sectional data from PharmaTrac, a nationally representative private-sector drug sales dataset gathered from a panel of 9000 stockists across India. We used the AWaRe (Access, Watch, Reserve) classification and the defined daily dose (DDD) metrics to calculate the per capita private-sector consumption of systemic antibiotics across different categories: FDCs vs single formulations; approved vs unapproved; and listed vs not listed in the national list of essential medicines (NLEM). Findings: The total DDDs consumed in 2019 was 5071 million (10.4 DDD/1000/day). Watch contributed 54.9% (2783 million) DDDs, while Access contributed 27.0% (1370 million). Formulations listed in the NLEM contributed 49.0% (2486 million DDDs); FDCs contributed 34.0% (1722 million), and unapproved formulations contributed 47.1% (2408 million DDDs). Watch antibiotics constituted 72.7% (1750 million DDDs) of unapproved products and combinations discouraged by the WHO constituted 48.7% (836 million DDDs) of FDCs. Interpretation: Although the per-capita private-sector consumption rate of antibiotics in India is relatively low compared to many countries, India consumes a large volume of broad-spectrum antibiotics that should ideally be used sparingly. This, together with significant share of FDCs from formulations outside NLEM and a large volume of antibiotics not approved by the central drug regulators, call for significant policy and regulatory reform. Funding: Not applicable.

Media Exposure and the Risk of Post-Traumatic Stress Disorder Following a Mass traumatic Event: An In-silico Experiment.

Introduction: Following mass traumatic events, greater exposure to traditional media like television (TV) about the event is associated with higher burden of post-traumatic stress disorder (PTSD). However, we know little about how social media exposure, combined with other media sources, shapes the population burden of PTSD following mass traumatic events. Materials and Methods: We built a microsimulation of 1,18,000 agents that was demographically comparable to the population of Parkland and Coral Springs, Florida that experienced the Stoneman Douglas High School shooting in 2018. We parametrized the model using data from prior traumatic events and built an internal social network structure to facilitate the estimation of community PTSD prevalence following exposure to TV and social media coverage of the shooting. Results: Overall, PTSD prevalence in the community due to exposure to TV coverage of the shooting was 3.1%. Shifting the whole population's hours of TV watching to the lower half of the population distribution decreased PTSD prevalence to 1.3% while increasing TV watching to the upper half of the distribution increased the prevalence to 3.5%. Casual (i.e., viewing posts) social media use in addition to exposure to TV coverage increased PTSD prevalence to 3.4%; overall prevalence increased to 5.3% when agents shared videos related to the shooting on social media. Conclusion: This microsimulation shows that availability and exposure to media coverage of mass traumatic events, particularly as social media becomes more ubiquitous, has the potential to increase community PTSD prevalence following these events. Future research could fruitfully examine the mechanisms that might explain these associations and potential interventions that can mitigate the role of media in shaping the mental health of populations following traumatic events.

Data Sources for Understanding the Social Determinants of Health: Examples from Two Middle-Income Countries: the 3-D Commission.

The expansion in the scope, scale, and sources of data on the wider social determinants of health (SDH) in the last decades could bridge gaps in information available for decision-making. However, challenges remain in making data widely available, accessible, and useful towards improving population health. While traditional, government-supported data sources and comparable data are most often used to characterize social determinants, there are still capacity and management constraints on data availability and use. Conversely, privately held data may not be shared. This study reviews and discusses the nature, sources, and uses of data on SDH, with illustrations from two middle-income countries: Kenya and the Philippines. The review highlights opportunities presented by new data sources, including the use of big data technologies, to capture data on social determinants that can be useful to inform population health. We conducted a search between October 2010 and September 2020 for grey and scientific publications on social determinants using a search strategy in PubMed and a manual snowball search. We assessed data sources and the data environment in both Kenya and the Philippines. We found limited evidence of the use of new sources of data to study the wider SDH, as most of the studies available used traditional sources. There was also no evidence of qualitative big data being used. Kenya has more publications using new data sources, except on the labor determinant, than the Philippines. The Philippines has a more consistent distribution of the use of new data sources across the HEALTHY determinants than Kenya, where there is greater variation of the number of publications across determinants. The results suggest that both countries use limited SDH data from new data sources. This limited use could be due to a number of factors including the absence of standardized indicators of SDH, inadequate trust and acceptability of data collection methods, and limited infrastructure to pool, analyze, and translate data.

Use of Data to Understand the Social Determinants of Depression in Two Middle-Income Countries: the 3-D Commission.

Depression accounts for a large share of the global disease burden, with an estimated 264 million people globally suffering from depression. Despite being one of the most common kinds of mental health (MH) disorders, much about depression remains unknown. There are limited data about depression, in terms of its occurrence, distribution, and wider social determinants. This work examined the use of novel data sources for assessing the scope and social determinants of depression, with a view to informing the reduction of the global burden of depression.This study focused on new and traditional sources of data on depression and its social determinants in two middle-income countries (LMICs), namely, Brazil and India. We identified data sources using a combination of a targeted PubMed search, Google search, expert consultations, and snowball sampling of the relevant literature published between October 2010 and September 2020. Our search focused on data sources on the following HEALTHY subset of determinants: healthcare (H), education (E), access to healthy choices (A), labor/employment (L), transportation (T), housing (H), and income (Y).Despite the emergence of a variety of data sources, their use in the study of depression and its HEALTHY determinants in India and Brazil are still limited. Survey-based data are still the most widely used source. In instances where new data sources are used, the most commonly used data sources include social media (twitter data in particular), geographic information systems/global positioning systems (GIS/GPS), mobile phone, and satellite imagery. Often, the new data sources are used in conjunction with traditional sources of data. In Brazil, the limited use of new data sources to study depression and its HEALTHY determinants may be linked to (a) the government's outsized role in coordinating healthcare delivery and controlling the data system, thus limiting innovation that may be expected from the private sector; (b) the government routinely collecting data on depression and other MH disorders (and therefore, does not see the need for other data sources); and (c) insufficient prioritization of MH as a whole. In India, the limited use of new data sources to study depression and its HEALTHY determinants could be a function of (a) the lack of appropriate regulation and incentives to encourage data sharing by and within the private sector, (b) absence of purposeful data collection at subnational levels, and (c) inadequate prioritization of MH. There is a continuing gap in the collection and analysis of data on depression, possibly reflecting the limited priority accorded to mental health as a whole. The relatively limited use of data to inform our understanding of the HEALTHY determinants of depression suggests a substantial need for support of independent research using new data sources. Finally, there is a need to revisit the universal health coverage (UHC) frameworks, as these frameworks currently do not include depression and other mental health-related indicators so as to enable tracking of progress (or lack thereof) on such indicators.

Food, Big Data, and Decision-making: a Scoping Review-the 3-D Commission.

Food is an important determinant of health, featuring prominently in the Sustainable Development Goals. The term "big data" is seldom used in relation to food, partly because food data are scattered across different sectors. The increasing availability of food-related data presents an opportunity to glean new insights on food and food systems. These insights may enhance the quality of products and services and improve decision-making on optimizing food availability, all to the end of producing better health. Yet, knowledge gaps remain about the unique opportunities and challenges linked to big data on food and their use in decision-making. This scoping review explored the available literature linking food with big data and decision-making, using the following research question: What is the current literature on data about food, and how are these data used in decision-making? We searched PubMed until 29 February 2020 and Embase, Web of Sciences, and the Cochrane Database of Systematic Reviews until 8 March 2020. We included studies written in English and conducted narrative analyses to identify relevant themes from included studies. Sixteen studies fulfilled our eligibility criteria, including big data analyses, modelling studies, and reviews. These studies described the added value of using big data and how evidence from big data had or can be used for decision-making, as well as challenges and opportunities for such use. The majority of the included studies examined the link between food and big data, while hypothesizing of how these insights could inform decision-making, including policies, interventions, programs, and financing. There were only two examples wherein big data on food informed decision-making directly. The review highlights several false dichotomies in how the subject is approached in the literature and the importance of context, both between and within countries, in shaping the availability and types of data that can be used as meaningful evidence to inform decision-making. This review shows the paucity of research around the intersection of food, big data, and decision-making, as well as the potential in using big data on food systems to the end of informing decisions to improve the health of populations. Future research and decision-making around health systems can benefit from examining the full spectrum of perspectives on the subject. Future research and decision-making around health systems can also employ the steadfast embrace of technology, which will potentially reduce disparities in big data availability, to the end of improving the health of populations.

Social media, vaccine hesitancy and trust deficit in immunization programs: a qualitative enquiry in Malappuram District of Kerala, India.

BACKGROUND: With increased penetration of the internet and social media, there are concerns regarding its negative role in influencing parents' decisions regarding vaccination for their children. It is perceived that a mix of religious reasons and propaganda by anti-vaccination groups on social media are lowering the vaccination coverage in Malappuram district of Kerala. We undertook a qualitative study to understand the factors responsible for generating and perpetuating vaccine hesitancy, the pathways of trust deficit in immunization programs and the interaction between various social media actors. METHODS: In-depth interviews and focus group discussions were conducted among parents/caregivers, physicians, public sector health staff, alternative system medical practitioners, field healthcare workers and teachers in areas with highest and lowest vaccination coverage in the district, as well as with communication experts. RESULTS: The trust deficit between parents/caregivers and healthcare providers is created by multiple factors, such as providers' lack of technical knowledge, existing patriarchal societal norms and critical views of vaccine by naturopaths and homeopaths. Anti-vaccine groups use social media to influence caregivers' perceptions and beliefs. Religion does not appear to play a major role in creating vaccine resistance in this setting. CONCLUSIONS: A long-term, multipronged strategy should be adopted to address the trust deficit. In the short to medium term, the health sector can focus on appropriate and targeted vaccine-related communication strategies, including the use of infographics, soft skills training for healthcare workers, technical competency improvement through a mobile application-based repository of information and creation of a media cell to monitor vaccine-related conversations in social media and to intervene if needed.

Energy, Data, and Decision-Making: a Scoping Review-the 3D Commission.

Access to energy is an important social determinant of health, and expanding the availability of affordable, clean energy is one of the Sustainable Development Goals. It has been argued that climate mitigation policies can, if well-designed in response to contextual factors, also achieve environmental, economic, and social progress, but otherwise pose risks to economic inequity generally and health inequity specifically. Decisions around such policies are hampered by data gaps, particularly in low- and middle-income countries (LMICs) and among vulnerable populations in high-income countries (HICs). The rise of "big data" offers the potential to address some of these gaps. This scoping review sought to explore the literature linking energy, big data, health, and decision-making.Literature searches in PubMed, Embase, and Web of Science were conducted. English language articles up to April 1, 2020, were included. Pre-agreed study characteristics including geographic location, data collected, and study design were extracted and presented descriptively, and a qualitative thematic analysis was performed on the articles using NVivo.Thirty-nine articles fulfilled eligibility criteria. These included a combination of review articles and research articles using primary or secondary data sources. The articles described health and economic effects of a wide range of energy types and uses, and attempted to model effects of a range of technological and policy innovations, in a variety of geographic contexts. Key themes identified in our analysis included the link between energy consumption and economic development, the role of inequality in understanding and predicting harms and benefits associated with energy production and use, the lack of available data on LMICs in general, and on the local contexts within them in particular. Examples of using "big data," and areas in which the articles themselves described challenges with data limitations, were identified.The findings of this scoping review demonstrate the challenges decision-makers face in achieving energy efficiency gains and reducing emissions, while avoiding the exacerbation of existing inequities. Understanding how to maximize gains in energy efficiency and uptake of new technologies requires a deeper understanding of how work and life is shaped by socioeconomic inequalities between and within countries. This is particularly the case for LMICs and in local contexts where few data are currently available, and for whom existing evidence may not be directly applicable. Big data approaches may offer some value in tracking the uptake of new approaches, provide greater data granularity, and help compensate for evidence gaps in low resource settings.

Capacity-need gap in hospital resources for varying mitigation and containment strategies in India in the face of COVID-19 pandemic.

BACKGROUND: Due to uncertainties encompassing the transmission dynamics of COVID-19, mathematical models informing the trajectory of disease are being proposed throughout the world. Current pandemic is also characterized by surge in hospitalizations which has overwhelmed even the most resilient health systems. Therefore, it is imperative to assess health system preparedness in tandem with need projections for comprehensive outlook. OBJECTIVE: We attempted this study to forecast the need for hospital resources for one year period and correspondingly assessed capacity and tipping points of Indian health system to absorb surges in need due to COVID-19. METHODS: We employed age-structured deterministic SEIR model and modified it to allow for testing and isolation capacity to forecast the need under varying scenarios. Projections for documented cases were made for varying degree of containment and mitigation strategies. Correspondingly, data on health resources was collated from various government records. Further, we computed daily turnover of each of these resources which was then adjusted for proportion of cases requiring mild, severe and critical care to arrive at maximum number of COVID-19 cases manageable by health care system of India. FINDINGS: Our results revealed pervasive deficits in the capacity of public health system to absorb surge in need during peak of epidemic. Also, model suggests that continuing strict lockdown measures in India after mid-May 2020 would have been ineffective in suppressing total infections significantly. Augmenting testing to 1,500,000 tests per day during projected peak (mid-September) under social-distancing measures and current test to positive rate of 9.7% would lead to more documented cases (60, 000, 000 to 90, 000, 000) culminating to surge in demand for hospital resources. A minimum allocation of 13x, 70x and 37x times more beds for mild cases, ICU beds and mechanical ventilators respectively would be required to commensurate with need under that scenario. However, if testing capacity is limited to 9,000,000 tests per day (current situation as of 19th August 2020) under continued social-distancing measures, documented cases would plummet significantly, still requiring 5x, 31x and 16x times the current allocated resources (beds for mild cases, ICU beds and mechanical ventilators respectively) to meet unmet need for COVID-19 treatment in India.

COVID-19: Challenges and its consequences for rural health care in India.

This commentary highlights the potential consequences of the COVID-19 pandemic for India's rural population. The rural health care system in India is not adequate or prepared to contain COVID-19 transmission, especially in many densely populated northern Indian States because of the shortage of doctors, hospital beds, and equipment. The COVID-19 pandemic creates a special challenge due to the paucity of testing services, weak surveillance system and above all poor medical care. The impacts of this pandemic, and especially the lockdown strategy, are multi-dimensional. The authors argue for the need to take immediate steps to control the spread and its aftereffects and to use this opportunity to strengthen and improve its primary health care system in rural India.